Triple

T34015594
Position Surface form Disambiguated ID Type / Status
Subject Mexican Federal Highway 37D E872232 entity
Predicate parallelTo P1868 FINISHED
Object Mexican Federal Highway 37
Mexican Federal Highway 37 is a major federal roadway in Mexico that serves as an important north–south transportation corridor in the western part of the country.
 E2087627 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Mexican Federal Highway 37 | Statement: [Mexican Federal Highway 37D, parallelTo, Mexican Federal Highway 37]
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Mexican Federal Highway 37
Triple: [Mexican Federal Highway 37D, parallelTo, Mexican Federal Highway 37]
Generated description
Mexican Federal Highway 37 is a major federal roadway in Mexico that serves as an important north–south transportation corridor in the western part of the country.

Provenance (5 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69f349a19ad88190ab586f010c804a8f completed April 30, 2026, 12:22 p.m.
NER Named-entity recognition batch_69f70af2f1888190a5509e1ac77075f5 completed May 3, 2026, 8:44 a.m.
NED1 Entity disambiguation (via context triple) batch_6a36d5cad87081908800a9d7bdd0cb2a completed June 20, 2026, 6:02 p.m.
NEDg Description generation batch_6a36d8485b6081908fd8cee9f02096e9 completed June 20, 2026, 6:13 p.m.
NED2 Entity disambiguation (via description) batch_6a36d8a830748190ba88df7e4555f7d5 completed June 20, 2026, 6:15 p.m.
Created at: May 1, 2026, 1:51 a.m.